End-to-end Keyword Spotting using Xception-1d
Not on the map. Why?
Abstract (arXiv)
The field of conversational agents is growing fast and there is an increasing need for algorithms that enhance natural interaction. In this work we show how we achieved state of the art results in the Keyword Spotting field by adapting and tweaking the Xception algorithm, which achieved outstanding results in several computer vision tasks. We obtained about 96% accuracy when classifying audio clips belonging to 35 different categories, beating human annotation at the most complex tasks proposed.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 18 references that are in the map. First posted on arXiv 9 Oct 2021.
Builds on
11 of 18 references.
References · 11
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,827
- 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,320
- 2018Speech Commands: A Dataset for Limited-Vocabulary Speech Recognitioncited by 741
- 2016Reinforcement Learning with Unsupervised Auxiliary Taskscited by 400
- 2019Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentationcited by 258
- 2017Hello Edge: Keyword Spotting on Microcontrollerscited by 108
- 2018A neural attention model for speech command recognitioncited by 44
- 2019Depthwise Convolution is All You Need for Learning Multiple Visual Domainscited by 36
- 2017Listening to the World Improves Speech Command Recognitioncited by 8
- 2018The Effectiveness of Instance Normalization: a Strong Baseline for Single Image Dehazingcited by 4
Built on by
Not cited yet.